Using the information from reference station networks: a novel approach conforming to RTCM V2.3 and future V3.0

Keenan, Chelsea; Zebhauser, B.E.; Euler, Hans-Jürgen; Wübbena, Gerhard

doi:10.1109/plans.2002.998925

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…The obtained results in this study are consistent with those of many other groups that made similar tests. The horizontal and vertical accuracy in obstructed areas discussed in this paper are in agreement with those of the other authors (Andersen et al 2009, Bakula et al 2015, Brach and Zasada 2014, Dobelis and Zvirgzds 2016, Frank and Wing 2014, Wing and Eklund 2007, Kaartinen et al 2015, Keenan et al 2002, Kim et al 2017, Mageed 2013, Massimiliano 2018, Naesset and Gievestad 2008.…”

Section: Resultssupporting

confidence: 92%

“…(Prochniewicz et al 2020). The utilization of the GNSS CORS network also enables for more reliable differential corrections across large regions, such as the Virtual Reference Station (VRS) technique (Wanninger, 2003), (Mageed, 2013) the Multi Reference Station (MRS) approach (Fotopoulus et al 2001), the Flächen Korrektur Parameter (FKP) approach or other surface correction approaches (Keenan et al, 2002), (Kim et al, 2017). Several authors (Prochniewicz et al, 2020), (Kim et al, 2017), (Dobelis et al 2016) demonstrated that the NRTK technique allows reaching centimeter accuracy, comparable with the accuracy of the static measurements.…”

Section: Introductionmentioning

confidence: 99%

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Network Real Time Kinematic (Cors-FKP Method) Accuracy in/Under Forest Area

Pirti¹,

Kurtulgu²

2023

AgricultForest

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The use of Global navigation satellite system (GNSS), (geolocation and navigation system) in/under forestry is increasingly being established, especially in the areas of logistics, inventory and measurements. However, the uninitialized forest user assumes that GNSS will always work with the same accuracy everywhere, while the more critical foresters assume that GNSS will probably not work properly in/under the forest. A criterion is often used to evaluate the performance of a GNSS receiver in an obstructed environment. Due to tree canopy in a forest, GNSS performance can be limited, disturbed or completely prevented. The Real Time Kinematics (RTK) approach can provide centimetrelevel accuracy in a suitable environment. A more accurate and stable positioning technique was first proposed in the mid-1990s by meticulously modelling all fault components based on a known reference network called the Network Based RTK (NRTK) technique. Since then, several methods have been developed and implemented in practice, such as the Virtual Reference Station (VRS), the Flächen Korrektur Parameter (FKP) and the Master-Auxiliary Concept (MAC), which are fundamentally similar approaches. The purpose of all studies for this paper was to evaluate the achievable accuracy of the Continuously Operating Reference Stations (CORS)-FKP in the project area and check the results. The CORS-FKP results show that the solution for the forest area was reached at cm level (±10-15 cm) for horizontal accuracy.

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Section: Resultssupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Network Real Time Kinematic (Cors-FKP Method) Accuracy in/Under Forest Area

Pirti¹,

Kurtulgu²

2023

AgricultForest

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“…The presence of widely spread GNSS CORS networks encouraged the use of the NRTK technique that allows overcoming the limits of the distances among the stations. The use of GNSS CORS network, also, allows applying differential corrections more reliable on wide areas, such as the Virtual Reference Station (VRS) approach [6], the Multi Reference Station (MRS) approach [7], the Flächen Korrektur Parameter (FKP) approach or other surface correction approaches [8,9]. Several authors [10][11][12] demonstrated that the NRTK technique allows reaching centimeter accuracy, comparable with the accuracy of the static measurements.…”

Section: Introductionmentioning

confidence: 99%

NRTK, PPP or Static, That Is the Question. Testing Different Positioning Solutions for GNSS Survey

et al. 2021

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Worldwide, the determination of the coordinates from a Global Navigation Satellite System (GNSS) survey (in Network Real Time Kinematic, Precise Point Positioning, or static mode) has been analysed in several scientific and technical applications. Many of those have been carried out to compare Precise Point Positioning (PPP), Network Real Time Kinematic (NRTK), and static modes’ solutions, usually, using the latter as the true or the most plausible solution. This approach is not always possible as the static mode solution depends on several parameters (baseline length, acquisition time, ionospheric, and tropospheric models, etc.) that must be considered to evaluate the accuracy of the method. This work aims to show the comparison among the GNSS survey methods mentioned above, using some benchmark points. The tests were carried out by comparing the survey methods in pairs to check their solutions congruence. The NRTK and the static solutions refer to a local GNSS CORS network’s analysis. The NRTK positioning has been obtained with different methods (VRS, FKP, NEA) and the PPP solution has been calculated with two different software (RTKLIB and CSRS-PPP). A statistical approach has been performed to check if the distribution frequencies of the coordinate’s residual belong to the normal distribution, for all pairs analysed. The results show that the hypothesis of a normal distribution is confirmed in most of the pairs and, specifically, the Static vs. NRTK pair seems to achieve the best congruence, while involving the PPP approach, pairs obtained with CSRS software achieve better congruence than those involving RTKLIB software.

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“…Euler et al (2004) discussed the impact of incorrectly determined network integer ambiguity on the separated dispersive and non-dispersive corrections. Keenan et al (2002) proposed a user standard correction transmission format that separates the network corrections. Dispersive and non-dispersive components have different dynamic effects.…”

Section: Introductionmentioning

confidence: 99%

Application of Running Average Function to Non-Dispersive Errors of Network-Based Real-Time Kinematic Positioning

Lim¹,

Rizos²,

Musa³

2008

J. of GPS

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The GPS errors can be separated into a frequency-dependent or dispersive component (e.g. the ionospheric delay) and a non-dispersive component (e.g. the tropospheric delay and orbit biases). Dispersive and nondispersive errors have different dynamic effects on the GPS network corrections. The former exhibit rapid changes with high variations due to the effect of free electrons in the ionosphere, whilst the latter change slowly and smoothly over time due to the characteristic behaviour of the tropospheric delay and the nature of orbit biases. A running average is proposed in this paper to provide a stable network correction for the non-dispersive term. It is found that the non-dispersive correction can be used to obtain better ionosphere-free measurements, and therefore helpful in resolving the long-range integer ambiguity of the GPS carrier-phase measurements. Once the integer ambiguities have been resolved, dispersive and non-dispersive corrections can be applied to the fixed carrier-phase measurements for positioning step so as to improve the accuracy of the estimated coordinates. Instantaneous positioning i.e. single-epoch positioning, has been tested for two regional networks: SydNET, Sydney, and SIMRSN, Singapore. The test results have shown that the proposed strategy performs well in generating the network corrections, fixing ambiguities and computing a user's position.

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Using the information from reference station networks: a novel approach conforming to RTCM V2.3 and future V3.0

Cited by 5 publications

References 4 publications

Network Real Time Kinematic (Cors-FKP Method) Accuracy in/Under Forest Area

Network Real Time Kinematic (Cors-FKP Method) Accuracy in/Under Forest Area

NRTK, PPP or Static, That Is the Question. Testing Different Positioning Solutions for GNSS Survey

Application of Running Average Function to Non-Dispersive Errors of Network-Based Real-Time Kinematic Positioning

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